Measurements, Models, Systems and Design

531 s.

Conception des chaînes logistiques multicritères avec prise en compte des incertitudes

Les modèles de conception des chaînes logistiques sont devenus de plus en plus complexes, à cause de l'environnement économique incertain et l'introduction de nouveaux critères de décision tels que : l'aspect environnemental, l'aspect social, l'aspect législatif, l'aspect économique, la satisfaction du client et la prise en compte des risques. Répondre aux changements qui touchent les chaînes logistiques exige de composer avec des incertitudes et des informations incomplètes. Configurer des chaînes logistiques multicritères avec prise en compte des incertitudes peut garantir la continuité des activités de l'entreprise.L'objectif principal de cette thèse est la conception de chaînes logistiques multicritères qui résistent aux changements et l'instabilité des marchés. Le manuscrit de cette thèse s'articule autour de sept principaux chapitres:1 - introduction.2 - Etat de l'art sur la conception des chaînes logistiques.3 -Conception des chaînes logistiques multicritères en mesure de répondre aux nouveauxcritères économiques, sociaux, environnementaux et législatifs.4 - Conception des chaînes logistiques multi-objectifs.5 - Développement d'une heuristique de résolution des problèmes de conception deschaînes logistiques de taille réelle.6 - Conception des chaînes logistiques avec prise en compte des incertitudes.7 - Conclusions et perspectives.This thesis contributes to the debate on how uncertainty and concepts of sustainable development can be put into modern supply chain network and focuses on issues associated with the design of multi-criteria supply chain network under uncertainty. First, we study the literature review , which is a review of the current state of the art of Supply Chain Network Design approaches and resolution methods. Second, we propose a new methodology for multi-criteria Supply Chain Network Design (SCND) as well as its application to real Supply Chain Network (SCN), in order to satisfy the customers demand and respect the environmental, social, legislative, and economical requirements. The methodology consists of two different steps. In the first step, we use Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) to buildthe model. Then, in the second step, we establish the optimal supply chain network using Mixed Integer Linear Programming model (MILP). Third, we extend the MILP to a multi-objective optimization model that captures a compromisebetween the total cost and the environment influence. We use Goal Programming approach seeking to reach the goals placed by Decision Maker. After that, we develop a novel heuristic solution method based on decomposition technique, to solve large scale supply chain network design problems that we failed to solve using exact methods. The heuristic method is tested on real case instances and numerical comparisons show that our heuristic yield high quality solutions in very limited CPU time. Finally, again, we extend the MILP model presented before where we assume that the costumer demands are uncertain. We use two-stage stochastic programming approach to model the supply chain network under demand uncertainty. Then, we address uncertainty in all SC parameters: opening costs, production costs, storage costs and customers demands. We use possibilistic linear programming approach to model the problem and we validate both approaches in a large application case.ARRAS-Bib.electronique (620419901) / SudocSudocFranceF

The Third Air Force/NASA Symposium on Recent Advances in Multidisciplinary Analysis and Optimization

The third Air Force/NASA Symposium on Recent Advances in Multidisciplinary Analysis and Optimization was held on 24-26 Sept. 1990. Sessions were on the following topics: dynamics and controls; multilevel optimization; sensitivity analysis; aerodynamic design software systems; optimization theory; analysis and design; shape optimization; vehicle components; structural optimization; aeroelasticity; artificial intelligence; multidisciplinary optimization; and composites

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms